Optimizing queue efficiency: Artificial intelligence-driven tandem queues with reneging

Abstract

This paper delves into the theoretical integration of queueing theory and artificial intelligence (AI), examining the benefits and implications of their convergence. Queueing systems serve as fundamental models for various real-world applications, from telecommunications networks to healthcare facilities. This research presents a transformative framework for elevating the efficiency and performance of queueing systems by infusing AI-driven tandem queue analysis. The implications of this approach transcend industries, promising streamlined operations, reduced waiting times, and resource optimization. This work invites further exploration and application, offering a path to more effective and responsive queueing systems globally. Over the years, researchers and practitioners have explored numerous techniques to enhance the efficiency and performance of queueing systems. In recent times, integrating AI into the realm of queueing analysis has opened up new avenues for optimization and innovation. This paper studies a two-server tandem queueing model with reneging customers using AI techniques. Assuming that the arrival rate follows the Poisson process and the service rate follows an exponential distribution, using the birth-death process, probability generating function and AI module, we derive steady-state difference equation, expected number of people in customers, and mean waiting time.